Brittleness Index of High-Rank Coal Reservoir and Its Influencing Factors in Mabidong Block, Qinshui Basin, China

Abstract

Brittleness index is an important mechanical index to evaluate the fracturability of conventional oil and gas reservoirs. However, the brittleness index of an organic coal reservoir is more complex. In this study, based on array sonic logging and density logging data, coal brittleness index is calculated using an elastic parameter method in the Mabidong coalbed methane (CBM) block in southern Qinshui Basin. In combination with coal-body structure observation, the maceral analysis, and proximate data of coal cores, a comprehensive study of geological influencing factors of coal brittleness index has been carried out. According to coal fragmentation degree, the coal-body structure of coal seams can be divided into primary, cataclastic, and granulate structure. The average interval of brittleness index of primary, cataclastic, and granulate structural coals is 63.3-71.48, 73.01-74.85, and 77.41-82.77, respectively. The results indicate that generally the order of brittleness index is primary structural coal < cataclastic structural coal < granulate structural coal. Young’s modulus and the brittleness index have a good positive correlation. Poisson’s ratio and the brittleness index are negatively correlated in No. 3 coal seam but are positive correlated in No. 15 coal seam. The vitrinite content is positively correlated with brittleness index, and the inertinite content is negatively correlated. For the primary and cataclastic structure coal, the ash and volatile is positively correlated with the brittleness index. The correlation of brittleness index, macerals, and coal quality parameters in the primary structure coal is better than that of the cataclastic and granulate structure coal. The research results are helpful to guide the coal brittleness index and coal-body structure prediction in fracturing of CBM wells.

Role of maceral groups in coal beneficiation: A short review

“…Hou et al (2020) measured the hardness and elastic modulus of vitrinite, inertinite and liptinite by depth-sensing nanoindentation, and the hardness values of the three maceral groups were 0.52GPa~0.78 GPa, 0.78 GPa ~1.37 GPa and 0.18 GPa ~0.52 GPa, respectively; the elastic modulus of the three maceral groups was 5.46 GPa ~6.97 GPa, 7.19 GPa ~8.91 GPa and 2.60 GPa ~4.42 GPa. Zhang (2021b) found that the maceral group content also affects the Young's modulus and Poisson's ratio of coal. They calculated the brittleness index of coal by equations 1-3.…”

Prediction Method of Coal Texture Considering Longitudinal Resolution of Logging Curves and Its Application─Taking No. 15 Coal Seam in the Shouyang Block as an Example

“…Therefore, it is urgent to establish an effective method for distinguishing coal texture. Logging curves, as an indirect parameter for identifying coal texture, have applicability advantages compared to identifying coal texture with coal cores, which may be affected by drilling and lead to misjudgment . Early research on coal texture prediction based on logging curves primarily employed cluster analysis and the Protodyakonov’s coefficient method .…”

“…Logging curves, as an indirect parameter for identifying coal texture, have applicability advantages compared to identifying coal texture with coal cores, which may be affected by drilling and lead to misjudgment. 4 Early research on coal texture prediction based on logging curves primarily employed cluster analysis 5 and the Protodyakonov’s coefficient method. 6 Subsequently, methods such as Archie’s formula, 7 coal texture index, 8 brittleness index, 9 geological strength index (GSI), 10 K-means algorithm, 11 principal component analysis, 12 neural network, 13 , 14 Fisher discriminant analysis, 15 and other quantitative classification techniques were gradually introduced.…”